Exploring the impact of body mass index on tumor biology and cancer development

Exploring the impact of body mass index on tumor biology and cancer development | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring the impact of body mass index on tumor biology and cancer development Johanne Ahrenfeldt, Stine Carstensen, Ida Maria Hemdorff Eriksen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4459331/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Jul, 2024 Read the published version in Journal of Cancer Research and Clinical Oncology → Version 1 posted 7 You are reading this latest preprint version Abstract Purpose Cancer continues to be a major global health challenge, affecting millions of individuals and placing substantial burdens on healthcare systems worldwide. Recent research suggests a complex relationship between obesity and cancer, with obesity increasing the risk of various cancers while potentially improving outcomes for diagnosed patients, a phenomenon termed the "obesity paradox". In this study, we used a cohort of 1,781 patients to investigate the impact of obesity on tumor characteristics, including gene expression, pathway dysfunction, genetic alterations and immune infiltration. Methods Patient samples spanned 10 different cancer types, and were obtained from the Cancer Genome Atlas, with annotations for body mass index (BMI), age, sex, tumor size and tumor gene expression data. Results When we compared the proportion of large (T3-T4) to small tumors (T1-T2) between obese and non-obese patients, we found that obese patients tended to present with smaller, less invasive tumors and exhibited distinct gene expression profiles, particularly in metabolic and proliferative pathways. Moreover, smaller tumors in obese patients show higher immune cell infiltration and increased T cell diversity, suggesting enhanced immune activity. Conclusion Taken together, these findings highlight the influence of obesity on tumor biology, with implications for personalized treatment strategies that consider patient physiology alongside tumor characteristics. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Cancer, characterized by uncontrolled cell growth and proliferation, remains a significant global health concern, posing substantial challenges to both healthcare systems and individuals. Understanding the multifaceted factors influencing cancer development is crucial for devising effective prevention and intervention strategies. One emerging area of research centers around the association between obesity and cancer. Obesity, resulting from an imbalance between energy intake and expenditure, has reached epidemic proportions worldwide. In 2016, 650 million people in the world were characterized as obese according to the World Health Organization (WHO). Beyond its established role in metabolic disorders, evidence has been mounting on the potential link between obesity and cancer incidence 1 – 6 . Numerous epidemiological and metastudies have suggested that obesity is associated with an increased risk of several cancer types, including breast, colorectal, and renal cancers 1 , 6 – 8 While the exact mechanisms underlying this association are still under investigation, chronic inflammation, altered hormonal profiles, and insulin resistance are among the proposed pathways through which obesity may contribute to tumorigenesis 9 – 11 Despite the established link between obesity and increased cancer risk, an intriguing phenomenon known as the “obesity paradox" has been observed across cancer types 12 – 14 . Paradoxically, while obesity may increase the likelihood of developing certain types of cancer, several studies have suggested that obese individuals with a cancer diagnosis may experience improved outcomes compared to their non-obese counterparts. The cause of this phenomenon is unknown, but it has been speculated to be due to increased resilience to treatment among obese individuals 15 or due to obese physiology inducing cancer metabolic profiles that reduce aggressive growth 16 . In the context of cancer aggressiveness, there is a growing interest in elucidating whether obesity influences the development of cancer with distinct phenotypic characteristics. While previous work has predominantly focused on the association between obesity and overall cancer risk, investigating the specific impact of obesity on specific cancer subtypes, potentially those exhibiting reduced aggressiveness, is essential for a more nuanced understanding of the impact of obesity on cancer development and outcome. Addressing this knowledge gap is critical not only for discerning the molecular and cellular underpinnings of obesity-related carcinogenesis, but also for tailoring treatment strategies to specific phenotypic features of obesity-induced cancer. By exploring whether obesity plays a role in the development of aggressive cancer phenotypes, it may open up novel avenues for cancer prevention and treatment within an increasing population of obese individuals. One of the important hallmarks of cancer is immune evasion 17 , as the immune system also works as a defense against development of cancer. The cancer cells must develop mechanisms to avoid the immune system in order to survive. An important part of the immune system's defense against cancer are cytotoxic T cells, capable of recognizing and killing cancer cells. T cells harbor the T-cell receptor (TCR), which recognizes mutation-induced neo-antigens produced by the cancer cells. A recent study suggests that a greater TCR diversity in the tumor is associated with a highly activated tumor microenvironment 18 . With this study, we used gene expression data obtained from 10,783 patients from the Cancer Genome Atlas to investigate if tumors from obese patients displayed phenotypic variation relative to tumors from non-obese patients. Across cancer types, we observed that tumors from obese patients were significantly smaller at diagnosis, and showed significantly altered gene expression patterns, particularly affecting genes in metabolic and proliferative pathways. Furthermore, through analysis of T cell receptor diversity, we infer likely variation in immunological profiles between tumors from obese and non-obese individuals. Overall, our work demonstrates that obesity itself significantly impacts not only the risk of developing cancer, but also the type of cancer, with likely implications for patient treatment decisions and prognosis. Methods Data Clinical information from 10,783 sequenced tumor samples from 33 different cancer types was acquired from The Cancer Genome Atlas 19 . RNAseq-based gene expression data which had been uniformly normalized for all samples was acquired from the University of California Santa Cruz (UCSC) Xena database 20 . Pathological T-stage was used as a measurement for tumor size and invasiveness. After omitting missing values from the following variables: T-stage, age, and sex; the data set contained 7309 cancer patients and 23 cancer types, this will be referred to as Subset 1. Of these, a subset of 1781 cancer patients from 10 cancer types were annotated with BMI values, when excluding extreme outlier values (BMI below 15 or above 60), this will be referred to as Subset 2. Additionally, T cell Receptor (TCR) diversity was available for 5,366 patients, obtained from Thorsson et al. 21 . Gene Sets and Immune Cell decomposition Gene set variation analysis (GSVA) 22 was performed to generate values for 50 Hallmark pathways from Liberzon et al. 23 from the gene expression data. Tumor immune cell decomposition was calculated as the tumor infiltrating leukocytes (TIL) score defined by Danaher et al 24 on whole tumor RNAseq data using the method described in Rosenthal et al 25 . Enrichment analysis For the enrichment analysis we looked at cancer driver mutations. Mutations were annotated as driver events using Annovar 26 as previously described in Ahrenfeldt et al 27 . Briefly we used PolyPhen 28 and SIFT 29 to predict if mutations were deleterious (tumor suppressor genes) or pathogenic (oncogenes). Enrichment analysis was performed using a two-sided Fisher exact test to compare large tumors to small tumors, on the number of patients with and without altered genes, per cancer type. The P values were corrected by false discovery rate (FDR) and a corrected P value below 0.05 was considered significant. The driver weight was calculated for each patient as 1/number of driver mutations, and then we calculated the mean difference in driver weight between small and large tumors per gene per cancer type. The P value for each gene-cancertype pair was calculated using a Wilcoxon rank sum test, and then corrected using FDR, a corrected P value below 0.05 was considered significant . Statistical analysis All data analysis was performed in R version 4.3.0 30 , using tidyverse 31 , survminer 32 , survival 33 , scales 34 , ggpubr 35 , ggAU-package 36 and Publish 37 . Survival analyses were performed by Cox proportional hazard regression 38 and Kaplan meier curves. Testing the significance of differences between groups was performed using the Wilcoxon rank sum test, unless otherwise mentioned. Fisher's exact test was used to determine if the proportion of small tumors was higher in a subset of the data. A binomial test was performed to test whether the distribution of cancer types which were significantly higher expressed in small and large tumors for each hallmark was significantly different from 50/50. All p-values are two-sided. Results Patients and samples To investigate the association between obesity and tumor aggression and size, we performed transcriptional pathway analysis and statistical analysis on data from The Cancer Genome Atlas (TCGA). From the full data set with 10783, we defined two nested subsets of data. Subset 1 consisted of 7309 patients spanning 23 different cancer types, all annotated with information on age, sex, and pathological tumor stage (T-stage). Subset 2 consisted of 1781 patients, all from Subset 1, who had Body Mass Index (BMI) information available, these patients spanned 10 cancer types (Fig. 1 ). Patients with high BMI more commonly harbor smaller, less invasive tumors First, we endeavored to investigate if obese individuals in general present with smaller tumors, indicative of a less aggressive phenotype driving early cancer development. To explore this, we used pathological T stage as a proxy for tumor size. T stage is a component of the standardized TNM (Tumor, Node, Metastasis) staging system developed by the American Joint Committee on Cancer (AJCC) and used globally for staging cancers 39 . As part of this, T stage describes the size and extent of the primary tumor and is typically graded as T1-T4. While the exact definition varies by cancer type, T1 tumors are typically smaller, while T4 tumors are larger and may have more extensive growth into local tissue. When we compared the BMI of patients based on T stage, we found that patients with low T stage, particularly T1 tumors, had higher BMI relative to patients with higher stage tumors (Fig. 2 A). When we stratified the patients into two groups based on T stage, small tumors (T1, T2) and large tumors (T3, T4), we found that patients with small tumors had a significantly higher BMI (median = 26.4), relative to patients with large tumors (median = 25.8, P = 0.0056) (Fig. 2 B). There was no significant difference in BMI by sex in this cohort (female median = 26, male median 26.2, P = 0.68). However, we observed the same pattern within each sex, where patients with small tumors had a significantly higher BMI relative to patients diagnosed with larger tumors (small tumors, female median = 26.4, male median 26.4; large tumors, female median = 25.7, male median 26, P female = 0.045, P male = 0.056, Fig. 2 C). When we further stratified patients based on BMI into obese (BMI > = 30) and non-obese (BMI < 30), we found a significant enrichment of small tumors in patients with obesity (Obese 57.2% vs non-obese 47.5%, P = 0.000427) (Fig. 2 d). Small tumors in patients with high BMI show unique immune profiles To investigate if smaller tumors from obese patients may be the result of more aggressive immune activity, we explored the differences in immune cell infiltration between small and large tumors from obese and non-obese patients. Given that the immune system decays with age due to immunosenescence 40 , we further stratified these analyses based on age. We investigated immune infiltration by utilizing the TIL score from Danaher 24 , and found that the small tumors of obese patients had a significantly higher level of immune infiltration relative to their non-obese counterparts (P = 0.00025), in younger (< 60 years) patients (Fig. 3 A). We observed no differences in immune infiltration within older patients nor between the larger tumors in patients with or without obesity (Fig. 3 B). Next, we investigated the composition of infiltrating immune cells using the ratio of adaptive to innate immune cells (A/I ratio). We have previously shown that within tumors the A/I ratio is associated with improved survival 41 . Here, we found that in younger patients with small tumors, obese patients had a higher A/I ratio relative to non-obese patients (P = 0.041) (Fig. 3 C). We found no significant differences in the older patients (Fig. 3 D). To investigate the landscape of tumor infiltrating adaptive immune cells, we obtained TCR diversity and richness estimates from the TCGA data, previously published by Thorsson et al 21 . We found that small tumors exhibited a significantly higher TCR Shannon diversity index in younger patients with obesity relative to younger patients without obesity (P = 0.0067) (Fig. 3 E). We found no significant difference in the older cohort with small tumors or between obese and non-obese patients with large tumors, neither in the young nor old cohort (Fig. 3 F). Tumors from obese individuals show distinct pathway expression profiles Tumor size is strongly prognostic and is therefore likely associated with a more aggressive biological phenotype. To investigate this, we compared gene expression profiles between small and large tumors across the 7309 samples from 23 cancer types in Subset 1 with T stage annotations and compared large tumors to small tumors within each cancer type. For this analysis, we summarized gene expression to pathways, gene set variation analysis (GSVA) of the 50 hallmark pathways 23 . All pathways were tested for significant differential expression across all 23 cancer types. In this manner, we observed that 38 showed a significantly different expression between small and large tumors at least once, ranging from 0 to 15 significant pathways per cancer type (Fig S1 A). To summarize these results across cancer types, the hallmark pathways were scored as either significantly expressed or not significantly expressed in each cancer type, using an FDR adjusted p-value of 0.1 as cutoff. We then used a binomial test to determine if a hallmark pathway was significantly enriched across multiple cancer types. Here, we found that large tumors have a significantly higher expression of the EPITHELIAL_MESENCHYMAL_TRANSITION, ANGIOGENESIS, and HYPOXIA pathways, all of which have previously been associated with poor outcome and aggressive cancer 42 – 44 . Furthermore, we found large tumors to have a significantly higher expression of the GLYCOLYSIS metabolic pathway, whereas small tumors have a significantly higher expression of fatty acid metabolism (Fig. 4 A). We also found that proliferative pathways such as MYC_TARGETS_V1 and V2 and G2M_CHECKPOINT were most highly expressed in large tumors, although this was not significant. Next, to investigate the impact of obesity in tumor phenotype, we further explored if obesity might impact the observed differences between small and large tumors in Subset 1. By comparing gene expression data between obese and non-obese patients, within small and large tumors separately, we observe lower expression of the proliferative pathways (small tumors: MYC_TARGETS_V1 and V2, large tumors: E2F_TARGETS, MYC_TARGETS_V1 and G2M_CHECKPOINT) and higher expression of immune related pathways (small tumors: IL6_JAK_STAT3_SIGNALING, INFLAMMATORY_RESPONSE, COMPLEMENT and ALLOGRAFT_REJECTION, large tumors: COAGULATION) in both small (Fig. 4 B) and large tumors (Fig. 4 C) in obese patients. To investigate the cancer-specific origin of the differential expression, we stratified the analysis on cancer type and found that for small tumors, overexpression of the proliferative pathways in non-obese patients were predominantly driven by liver cancer, esophagus cancer and renal cancer (Fig S1 B). Likewise, overexpression of immune pathways in obese patients mostly originated from liver cancer and bladder cancer. In large tumors overexpression of the proliferative pathways in non-obese patients mostly originated from liver cancer and colon cancer, while overexpression of immune pathways in obese patients mostly originated from melanoma and uveal melanoma (Fig S1 C). To investigate if there were differences in gene expression between older and younger patients, we performed the analysis stratified into older and younger patients, as above. We found that when we compared RNA expression from small tumors between younger and older patients, tumors from younger patients had a higher expression of proliferative pathways, such as E2F_TARGETS, G2M_CHECKPOINT and MITOTIC_SPINDLE. Conversely, in small tumors from older patients we found a higher expression of metabolic pathways including XENOBIOTIC_METABOLISM, BILE_ACID_METABOLISM, FATTY_ACID_METABOLISM, HEME_METABOLISM and OXIDATIVE_PHOSPHORYLATION (Fig S2A). When we repeated the analysis in large tumors, we found that younger patients had a higher expression of TGF_BETA_SIGNALING and APICAL_JUNCTION while no pathways had a significantly higher expression in older patients (Fig S2B). Next, to investigate if there were any significant differences in the expression between the two sexes, we performed the same analysis stratified by sex. For this analysis we excluded sex-specific cancer types, BRCA, CESC, PRAD and TGCT. When we compared small tumors between male and female patients, we found no significant difference (Fig S3A). When we performed the analysis using large tumors, we found that 18 of the 50 pathways are significantly higher expressed in female patients compared to male patients (Fig S3B), these include mainly immune related pathways (INFLAMMATORY_RESPONSE, COMPLEMENT, IL6_JAK3_STAT_SIGNALING, ALLOGRAFT_REJECTION, INTERFERON_GAMMA_RESPONSE and COAGULATION) and signaling pathways (TNFA_SIGNALING_VIA_NKFB, IL2_STAT5_SIGNALING, KRAS_SIGNALING, ESTROGEN_REPONSE_EARLY and ESTROGEN_RESPONSE_LATE). Genotypic patterns in large vs small tumors To investigate if the landscape of cancer driver mutations might differ between small and large tumors, we categorized all mutations found within known cancer genes in tumors from Subset 1 into whether they were likely driver mutations or likely passenger mutations. We explored how often individual cancer driver mutations occurred together with other driver mutations within the same tumor. To investigate this, we defined a driver-weight score. The driver weight score was determined for each driver mutation, within each tumor, as simply 1/n driver . We then compared the differences in mean driver weight across genes and cancer types. We found that there were more genes with a significantly higher driver weight in small tumors relative to in large tumors (Fig. 5 A). Examples of these are PIK3CA in both BRCA and HNSC, LRP1B in both LUSC and BRCA and TP53 in HNSC and SKCM. However, TP53 also has a higher driver weight in Large MESO tumors. To investigate whether small tumors had a higher driver weight in general, we compared the driver weight of small vs large tumors for each cancer type, where the tumor's driver weight was the same as for each of its driver mutations 1/n driver . We investigated the mean difference in driver mutations between small and large tumors and found that in three cancer types (BRCA, HNSC and KIRC) large tumors had significantly higher number of driver mutations (Fig. 5 B). When we looked at the frequency of specific driver mutations between large and small tumors, we only found two significantly enrichment genes (Fig. 5 C), HRAS in small BLCA tumors and CDH1 in large BRCA tumors. Discussion Our study suggests a link between obesity and reduced tumor size as we found a significantly higher BMI in the patients with smaller, less invasive tumors, as represented by lower T stage. We also found an association between obesity and increased immune invasion and lower expression of proliferative pathways, suggesting that tumors in obese individuals may harbor less aggressive biology. Our results thus support previous work indicating that while the obesity induced chronic inflammatory state may support tumorigenesis, it may also limit tumor growth through immune effector mechanisms 45 Furthermore, we found an increased expression of metabolic pathways, including the fatty acid and bile acid pathway in small tumors and in obese patients. We found an increased expression of the glycolysis pathway in the large tumors. This may indicate that small tumors grow on fatty acid, whereas larger tumors preferentially utilize glucose, via glycolysis and then lactic acid fermentation, i.e. the Warburg effect 46 . It is possible that tumors develop to preferentially metabolize fatty acids due to a more plentiful supply of free fatty acids in the plasma of obese patients 47 . Previous studies have also found a high level of variation between tumors and the tumor microenvironment between men and women 41 . However, we do not find this difference between men and women, when we stratify based on BMI. Here, we found no difference in BMI between male and female cancer patients in the analyzed cohort. We also found the same distribution of BMI of patients with small or large tumors in male and female patients. Furthermore, when we investigated the differentially expressed pathways between male and female patients in small or large tumors, few differences were found. This indicates that the differential expression pattern that we found in small tumors in patients with obesity, was independent of sex. In our study, we found that the main genetic difference between small and large tumors was the number of driver mutations, as we found fewer driver mutations per tumor in small tumors. And when we investigate if specific mutations were enriched in small or large tumors, we found only two genes, HRAS in small bladder cancer tumors and CDH1 in large breast cancer tumors. This indicates that on a genomic level, there is no difference between the molecular drivers of cancer between small and large tumors. These results thus follow the pattern of previous research, where we and others have found that there is no significant difference between the cancer driver landscape between primary and metastatic tumors 27 , 48 . Taken together, we here demonstrate that obesity may affect tumor biology, our findings are thus important in the context of personalized medicine. We show an effect of the host physiology on both tumor microenvironment and molecular characteristics, thus providing a more nuanced understanding of how obesity might affect cancer development. Our work thus highlights the limits of a tumor-centric approach to tumor characterization, where patient prognosis and treatment is primarily determined from single tumor biopsies. Rather, these results indicate that a holistic approach is needed, where overall patient characteristics are considered in order to properly determine optimal patient care. Declarations Author Contribution JA and NJB was in charge of the study conception and design. Data analysis was done by JA, SC and IMHE. JA and SC prepared all figures. The first draft of the manuscript was written by JA and NJB, and all authors contributed to reviewing and editing the manuscript. All authors read and approved the final manuscript. Acknowledgement We would like to thank GenomeDK and Aarhus University for providing computational resources and support that contributed to these research results. References Renehan, A. G., Tyson, M., Egger, M., Heller, R. F. & Zwahlen, M. Body-mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies. Lancet 371 , 569–578 (2008). Ma, Y. et al. Obesity and risk of colorectal cancer: a systematic review of prospective studies. PLoS One 8 , e53916 (2013). Genkinger, J. M. et al. A pooled analysis of 14 cohort studies of anthropometric factors and pancreatic cancer risk. Int. J. 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Supplementary Files SupplementaryMaterial.pdf Cite Share Download PDF Status: Published Journal Publication published 27 Jul, 2024 Read the published version in Journal of Cancer Research and Clinical Oncology → Version 1 posted Editorial decision: Revision requested 08 Jun, 2024 Reviews received at journal 07 Jun, 2024 Reviewers agreed at journal 24 May, 2024 Reviewers invited by journal 23 May, 2024 Submission checks completed at journal 22 May, 2024 Editor assigned by journal 22 May, 2024 First submitted to journal 22 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4459331","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":309468577,"identity":"f42c56b4-9ee4-4658-b12d-3da33017ca7e","order_by":0,"name":"Johanne Ahrenfeldt","email":"","orcid":"","institution":"Aarhus University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Johanne","middleName":"","lastName":"Ahrenfeldt","suffix":""},{"id":309468578,"identity":"5b27779b-4ece-4e50-b16f-2b6b7b82c906","order_by":1,"name":"Stine Carstensen","email":"","orcid":"","institution":"Aarhus University","correspondingAuthor":false,"prefix":"","firstName":"Stine","middleName":"","lastName":"Carstensen","suffix":""},{"id":309468579,"identity":"117be695-82b6-4545-ace8-e3fcd7ccdb1f","order_by":2,"name":"Ida Maria Hemdorff Eriksen","email":"","orcid":"","institution":"Aarhus University","correspondingAuthor":false,"prefix":"","firstName":"Ida","middleName":"Maria Hemdorff","lastName":"Eriksen","suffix":""},{"id":309468580,"identity":"77d8c7df-f759-40b4-8d06-d1f4d76ec803","order_by":3,"name":"Nicolai Juul Birkbak","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIie3PsQrCMBCA4QsFXQKuFfEdIoGKUPosBiGujkKHXgm0k7ha8CXcHFsydOkDCLqI0F18AbVuIjVuDvnHI99xAbDZ/jGXIIkRWA8czF8jaUj6SIwJQENYThCMyHigkmu2Dzgvi1gvIBDYldNWMtkWKttVM8+rBOoNzATSOm8l7CAUOSeO7x0IagqOQHeOJiTy+aYhkSHZJdpjbkP0g8gvh50KRbKq5O7zL5SVPKH1tJ0c0wtZ7cPROtX6RpfhcN2VrJW8bwDo/PLeZrPZbJ+7A87iTMOP+vxDAAAAAElFTkSuQmCC","orcid":"","institution":"Aarhus University","correspondingAuthor":true,"prefix":"","firstName":"Nicolai","middleName":"Juul","lastName":"Birkbak","suffix":""}],"badges":[],"createdAt":"2024-05-22 08:26:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4459331/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4459331/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00432-024-05890-4","type":"published","date":"2024-07-27T16:15:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":58068041,"identity":"5a2f4757-bdea-499c-88bf-1bc607337daf","added_by":"auto","created_at":"2024-06-10 18:05:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":564192,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eData cohort.\u003c/strong\u003eA schematic representation of the full data set from TCGA and Thorsson \u003cem\u003eet al.\u003c/em\u003e 2018, and the two subsets that we perform the analysis on. For the full data set we have gene expression data and T-cell receptor diversity information. For Subset 1, which includes 7,309 of the patients from the full data set, we have pathological T-stage, age and sex annotations for all patients. For Subset 2, which includes 1,781 patients from Subset1, we have height and weight information for all patients at diagnosis. The figure was created using BioRender.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4459331/v1/1d1086895ceb37db7a415a1c.png"},{"id":58068857,"identity":"8b1e7bb5-0a01-4ae9-bb8e-b5e35d39b724","added_by":"auto","created_at":"2024-06-10 18:13:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1571593,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBMI and pathological T-stage on Subset 2.\u003c/strong\u003e A) Patients BMI stratified by their tumor’s pathological t-stage. Colored by tumor size (Small: T1 and T2, Large: T3 and T4). B) Patients BMI plotted stratified by their tumor size. C) Patients BMI plotted against their tumor size stratified by sex. D) Patients are stratified by obesity, BMI \u0026gt;= 30, and the proportion of small and large tumors are shown for each group.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4459331/v1/dd43f80cf414a765f9ee955f.png"},{"id":58068047,"identity":"e5b6d140-057c-4539-bb53-a7c2b1215b24","added_by":"auto","created_at":"2024-06-10 18:05:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1394542,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTumor immune infiltration and diversity. \u003c/strong\u003eA) Tumor Infiltrating leukocytes (TIL) score in the younger (\u0026lt;60 years) patients. The patients are stratified by tumor size and colored by obesity (non-obese: BMI \u0026lt; 30, obese: BMI \u0026gt;=30). B) TIL score in the older (\u0026gt;=60 years) patients. The patients are stratified by tumor size and colored by obesity. C) Adaptive/innate immune ratio of younger patients. The patients are stratified by tumor size and colored by obesity. The Y-axis is log2-scaled. D) Adaptive/innate immune ratio of older patients. The patients are stratified by tumor size and colored by obesity. The Y-axis is log2-scaled. E) T-cell receptor (TCR) shannon diversity of younger patients. The patients are stratified by tumor size and colored by obesity. F) TCR shannon diversity of older patients. The patients are stratified by tumor size and colored by obesity.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4459331/v1/d71ee88ff812b5eb5204abb0.png"},{"id":58068858,"identity":"7e83b99e-b4e7-45be-9aa9-cda75f947bb8","added_by":"auto","created_at":"2024-06-10 18:13:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":406451,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferences in pathway expression in small and large tumors. \u003c/strong\u003eA) A bar plot showing the pathways where there are more than 5 cancer types with an overexpression in small or large tumors, and the number of cancer types that are significantly overexpressed in either direction. An asterisk, *, marks the pathways where the the distribution of cancer types into small or large are significantly different from 50/50, given a binomial distribution. B) A volcano plot showing the difference of mean (and p-value given a t-test) GSVA values for each pathway between non-obese and obese patients with small tumors. The pathways are colored by their overall process category. C) A volcano plot showing the difference of mean (and p-value given a t-test) GSVA values for each pathway between non-obese and obese patients with large tumors. The pathways are colored by their overall process category.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4459331/v1/21723b474ba9d22242e355e1.png"},{"id":58068046,"identity":"494bfd07-d85a-471a-abfe-53ea78d98c5b","added_by":"auto","created_at":"2024-06-10 18:05:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":331281,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDriver genes and tumor size.\u003c/strong\u003e A) A volcano plot showing the mean difference of driver weight per gene, per cancer type between small and large tumors. The driver weight is 1/number of driver mutations per tumor. The p-value is calculated by a t-test. B) A volcano plot showing the mean difference in number of driver mutations per tumor for each cancer type between small and large tumors. The p-value is calculated by a t-test. C) A volcano plot showing the odds ratio for an enrichment of certain driver mutations in small or large tumors. Odd ratio and p-value is calculated by fisher’s exact test.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4459331/v1/4191b8e50fd441c0f74ef7e0.png"},{"id":61596167,"identity":"8565364b-0c08-463e-89e7-fec21b199ccc","added_by":"auto","created_at":"2024-08-01 17:25:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4590591,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4459331/v1/a43d14c5-a852-46f1-8356-46858d4e45c8.pdf"},{"id":58068044,"identity":"10eda35b-2932-4275-8699-6067363ffeeb","added_by":"auto","created_at":"2024-06-10 18:05:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":609647,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4459331/v1/8db896a72bd2bc9c937cbb67.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the impact of body mass index on tumor biology and cancer development","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer, characterized by uncontrolled cell growth and proliferation, remains a significant global health concern, posing substantial challenges to both healthcare systems and individuals. Understanding the multifaceted factors influencing cancer development is crucial for devising effective prevention and intervention strategies. One emerging area of research centers around the association between obesity and cancer. Obesity, resulting from an imbalance between energy intake and expenditure, has reached epidemic proportions worldwide. In 2016, 650\u0026nbsp;million people in the world were characterized as obese according to the World Health Organization (WHO). Beyond its established role in metabolic disorders, evidence has been mounting on the potential link between obesity and cancer incidence\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Numerous epidemiological and metastudies have suggested that obesity is associated with an increased risk of several cancer types, including breast, colorectal, and renal cancers\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e–\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e While the exact mechanisms underlying this association are still under investigation, chronic inflammation, altered hormonal profiles, and insulin resistance are among the proposed pathways through which obesity may contribute to tumorigenesis\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e–\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDespite the established link between obesity and increased cancer risk, an intriguing phenomenon known as the “obesity paradox\" has been observed across cancer types \u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e–\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Paradoxically, while obesity may increase the likelihood of developing certain types of cancer, several studies have suggested that obese individuals with a cancer diagnosis may experience improved outcomes compared to their non-obese counterparts. The cause of this phenomenon is unknown, but it has been speculated to be due to increased resilience to treatment among obese individuals\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e or due to obese physiology inducing cancer metabolic profiles that reduce aggressive growth\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the context of cancer aggressiveness, there is a growing interest in elucidating whether obesity influences the development of cancer with distinct phenotypic characteristics. While previous work has predominantly focused on the association between obesity and overall cancer risk, investigating the specific impact of obesity on specific cancer subtypes, potentially those exhibiting reduced aggressiveness, is essential for a more nuanced understanding of the impact of obesity on cancer development and outcome. Addressing this knowledge gap is critical not only for discerning the molecular and cellular underpinnings of obesity-related carcinogenesis, but also for tailoring treatment strategies to specific phenotypic features of obesity-induced cancer. By exploring whether obesity plays a role in the development of aggressive cancer phenotypes, it may open up novel avenues for cancer prevention and treatment within an increasing population of obese individuals.\u003c/p\u003e \u003cp\u003eOne of the important hallmarks of cancer is immune evasion\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, as the immune system also works as a defense against development of cancer. The cancer cells must develop mechanisms to avoid the immune system in order to survive. An important part of the immune system's defense against cancer are cytotoxic T cells, capable of recognizing and killing cancer cells. T cells harbor the T-cell receptor (TCR), which recognizes mutation-induced neo-antigens produced by the cancer cells. A recent study suggests that a greater TCR diversity in the tumor is associated with a highly activated tumor microenvironment\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWith this study, we used gene expression data obtained from 10,783 patients from the Cancer Genome Atlas to investigate if tumors from obese patients displayed phenotypic variation relative to tumors from non-obese patients. Across cancer types, we observed that tumors from obese patients were significantly smaller at diagnosis, and showed significantly altered gene expression patterns, particularly affecting genes in metabolic and proliferative pathways. Furthermore, through analysis of T cell receptor diversity, we infer likely variation in immunological profiles between tumors from obese and non-obese individuals. Overall, our work demonstrates that obesity itself significantly impacts not only the risk of developing cancer, but also the type of cancer, with likely implications for patient treatment decisions and prognosis.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eData\u003c/p\u003e\u003cp\u003eClinical information from 10,783 sequenced tumor samples from 33 different cancer types was acquired from The Cancer Genome Atlas\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. RNAseq-based gene expression data which had been uniformly normalized for all samples was acquired from the University of California Santa Cruz (UCSC) Xena database \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Pathological T-stage was used as a measurement for tumor size and invasiveness. After omitting missing values from the following variables: T-stage, age, and sex; the data set contained 7309 cancer patients and 23 cancer types, this will be referred to as Subset 1. Of these, a subset of 1781 cancer patients from 10 cancer types were annotated with BMI values, when excluding extreme outlier values (BMI below 15 or above 60), this will be referred to as Subset 2. Additionally, T cell Receptor (TCR) diversity was available for 5,366 patients, obtained from Thorsson et al.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eGene Sets and Immune Cell decomposition\u003c/p\u003e\u003cp\u003eGene set variation analysis (GSVA)\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e was performed to generate values for 50 Hallmark pathways from Liberzon et al. \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e from the gene expression data.\u003c/p\u003e\u003cp\u003eTumor immune cell decomposition was calculated as the tumor infiltrating leukocytes (TIL) score defined by Danaher et al\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e on whole tumor RNAseq data using the method described in Rosenthal et al\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eEnrichment analysis\u003c/p\u003e\u003cp\u003eFor the enrichment analysis we looked at cancer driver mutations. Mutations were annotated as driver events using Annovar\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e as previously described in Ahrenfeldt et al\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Briefly we used PolyPhen\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e and SIFT\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e to predict if mutations were deleterious (tumor suppressor genes) or pathogenic (oncogenes). Enrichment analysis was performed using a two-sided Fisher exact test to compare large tumors to small tumors, on the number of patients with and without altered genes, per cancer type. The P values were corrected by false discovery rate (FDR) and a corrected P value below 0.05 was considered significant.\u003c/p\u003e\u003cp\u003eThe driver weight was calculated for each patient as 1/number of driver mutations, and then we calculated the mean difference in driver weight between small and large tumors per gene per cancer type. The P value for each gene-cancertype pair was calculated using a Wilcoxon rank sum test, and then corrected using FDR, a corrected P value below 0.05 was considered significant .\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll data analysis was performed in R version 4.3.0\u003csup\u003e30\u003c/sup\u003e, using tidyverse\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, survminer\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, survival\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, scales\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, ggpubr\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, ggAU-package\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e and Publish\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSurvival analyses were performed by Cox proportional hazard regression\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e and Kaplan meier curves.\u003c/p\u003e\u003cp\u003eTesting the significance of differences between groups was performed using the Wilcoxon rank sum test, unless otherwise mentioned. Fisher's exact test was used to determine if the proportion of small tumors was higher in a subset of the data. A binomial test was performed to test whether the distribution of cancer types which were significantly higher expressed in small and large tumors for each hallmark was significantly different from 50/50. All p-values are two-sided.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePatients and samples\u003c/h2\u003e \u003cp\u003eTo investigate the association between obesity and tumor aggression and size, we performed transcriptional pathway analysis and statistical analysis on data from The Cancer Genome Atlas (TCGA). From the full data set with 10783, we defined two nested subsets of data. Subset 1 consisted of 7309 patients spanning 23 different cancer types, all annotated with information on age, sex, and pathological tumor stage (T-stage). Subset 2 consisted of 1781 patients, all from Subset 1, who had Body Mass Index (BMI) information available, these patients spanned 10 cancer types (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePatients with high BMI more commonly harbor smaller, less invasive tumors\u003c/h2\u003e \u003cp\u003eFirst, we endeavored to investigate if obese individuals in general present with smaller tumors, indicative of a less aggressive phenotype driving early cancer development. To explore this, we used pathological T stage as a proxy for tumor size. T stage is a component of the standardized TNM (Tumor, Node, Metastasis) staging system developed by the American Joint Committee on Cancer (AJCC) and used globally for staging cancers\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. As part of this, T stage describes the size and extent of the primary tumor and is typically graded as T1-T4. While the exact definition varies by cancer type, T1 tumors are typically smaller, while T4 tumors are larger and may have more extensive growth into local tissue. When we compared the BMI of patients based on T stage, we found that patients with low T stage, particularly T1 tumors, had higher BMI relative to patients with higher stage tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). When we stratified the patients into two groups based on T stage, small tumors (T1, T2) and large tumors (T3, T4), we found that patients with small tumors had a significantly higher BMI (median\u0026thinsp;=\u0026thinsp;26.4), relative to patients with large tumors (median\u0026thinsp;=\u0026thinsp;25.8, P\u0026thinsp;=\u0026thinsp;0.0056) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). There was no significant difference in BMI by sex in this cohort (female median\u0026thinsp;=\u0026thinsp;26, male median 26.2, P\u0026thinsp;=\u0026thinsp;0.68). However, we observed the same pattern within each sex, where patients with small tumors had a significantly higher BMI relative to patients diagnosed with larger tumors (small tumors, female median\u0026thinsp;=\u0026thinsp;26.4, male median 26.4; large tumors, female median\u0026thinsp;=\u0026thinsp;25.7, male median 26, P female\u0026thinsp;=\u0026thinsp;0.045, P male\u0026thinsp;=\u0026thinsp;0.056, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). When we further stratified patients based on BMI into obese (BMI\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;30) and non-obese (BMI\u0026thinsp;\u0026lt;\u0026thinsp;30), we found a significant enrichment of small tumors in patients with obesity (Obese 57.2% vs non-obese 47.5%, P\u0026thinsp;=\u0026thinsp;0.000427) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSmall tumors in patients with high BMI show unique immune profiles\u003c/h2\u003e \u003cp\u003eTo investigate if smaller tumors from obese patients may be the result of more aggressive immune activity, we explored the differences in immune cell infiltration between small and large tumors from obese and non-obese patients. Given that the immune system decays with age due to immunosenescence\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, we further stratified these analyses based on age. We investigated immune infiltration by utilizing the TIL score from Danaher\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, and found that the small tumors of obese patients had a significantly higher level of immune infiltration relative to their non-obese counterparts (P\u0026thinsp;=\u0026thinsp;0.00025), in younger (\u0026lt;\u0026thinsp;60 years) patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). We observed no differences in immune infiltration within older patients nor between the larger tumors in patients with or without obesity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eNext, we investigated the composition of infiltrating immune cells using the ratio of adaptive to innate immune cells (A/I ratio). We have previously shown that within tumors the A/I ratio is associated with improved survival\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Here, we found that in younger patients with small tumors, obese patients had a higher A/I ratio relative to non-obese patients (P\u0026thinsp;=\u0026thinsp;0.041) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). We found no significant differences in the older patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eTo investigate the landscape of tumor infiltrating adaptive immune cells, we obtained TCR diversity and richness estimates from the TCGA data, previously published by Thorsson et al \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. We found that small tumors exhibited a significantly higher TCR Shannon diversity index in younger patients with obesity relative to younger patients without obesity (P\u0026thinsp;=\u0026thinsp;0.0067) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). We found no significant difference in the older cohort with small tumors or between obese and non-obese patients with large tumors, neither in the young nor old cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eTumors from obese individuals show distinct pathway expression profiles\u003c/h2\u003e \u003cp\u003eTumor size is strongly prognostic and is therefore likely associated with a more aggressive biological phenotype. To investigate this, we compared gene expression profiles between small and large tumors across the 7309 samples from 23 cancer types in Subset 1 with T stage annotations and compared large tumors to small tumors within each cancer type. For this analysis, we summarized gene expression to pathways, gene set variation analysis (GSVA) of the 50 hallmark pathways\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. All pathways were tested for significant differential expression across all 23 cancer types. In this manner, we observed that 38 showed a significantly different expression between small and large tumors at least once, ranging from 0 to 15 significant pathways per cancer type (Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). To summarize these results across cancer types, the hallmark pathways were scored as either significantly expressed or not significantly expressed in each cancer type, using an FDR adjusted p-value of 0.1 as cutoff. We then used a binomial test to determine if a hallmark pathway was significantly enriched across multiple cancer types. Here, we found that large tumors have a significantly higher expression of the EPITHELIAL_MESENCHYMAL_TRANSITION, ANGIOGENESIS, and HYPOXIA pathways, all of which have previously been associated with poor outcome and aggressive cancer\u003csup\u003e\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Furthermore, we found large tumors to have a significantly higher expression of the GLYCOLYSIS metabolic pathway, whereas small tumors have a significantly higher expression of fatty acid metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). We also found that proliferative pathways such as MYC_TARGETS_V1 and V2 and G2M_CHECKPOINT were most highly expressed in large tumors, although this was not significant.\u003c/p\u003e \u003cp\u003eNext, to investigate the impact of obesity in tumor phenotype, we further explored if obesity might impact the observed differences between small and large tumors in Subset 1. By comparing gene expression data between obese and non-obese patients, within small and large tumors separately, we observe lower expression of the proliferative pathways (small tumors: MYC_TARGETS_V1 and V2, large tumors: E2F_TARGETS, MYC_TARGETS_V1 and G2M_CHECKPOINT) and higher expression of immune related pathways (small tumors: IL6_JAK_STAT3_SIGNALING, INFLAMMATORY_RESPONSE, COMPLEMENT and ALLOGRAFT_REJECTION, large tumors: COAGULATION) in both small (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB) and large tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC) in obese patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo investigate the cancer-specific origin of the differential expression, we stratified the analysis on cancer type and found that for small tumors, overexpression of the proliferative pathways in non-obese patients were predominantly driven by liver cancer, esophagus cancer and renal cancer (Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB). Likewise, overexpression of immune pathways in obese patients mostly originated from liver cancer and bladder cancer. In large tumors overexpression of the proliferative pathways in non-obese patients mostly originated from liver cancer and colon cancer, while overexpression of immune pathways in obese patients mostly originated from melanoma and uveal melanoma (Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eTo investigate if there were differences in gene expression between older and younger patients, we performed the analysis stratified into older and younger patients, as above. We found that when we compared RNA expression from small tumors between younger and older patients, tumors from younger patients had a higher expression of proliferative pathways, such as E2F_TARGETS, G2M_CHECKPOINT and MITOTIC_SPINDLE. Conversely, in small tumors from older patients we found a higher expression of metabolic pathways including XENOBIOTIC_METABOLISM, BILE_ACID_METABOLISM, FATTY_ACID_METABOLISM, HEME_METABOLISM and OXIDATIVE_PHOSPHORYLATION (Fig S2A). When we repeated the analysis in large tumors, we found that younger patients had a higher expression of TGF_BETA_SIGNALING and APICAL_JUNCTION while no pathways had a significantly higher expression in older patients (Fig S2B).\u003c/p\u003e \u003cp\u003eNext, to investigate if there were any significant differences in the expression between the two sexes, we performed the same analysis stratified by sex. For this analysis we excluded sex-specific cancer types, BRCA, CESC, PRAD and TGCT. When we compared small tumors between male and female patients, we found no significant difference (Fig S3A). When we performed the analysis using large tumors, we found that 18 of the 50 pathways are significantly higher expressed in female patients compared to male patients (Fig S3B), these include mainly immune related pathways (INFLAMMATORY_RESPONSE, COMPLEMENT, IL6_JAK3_STAT_SIGNALING, ALLOGRAFT_REJECTION, INTERFERON_GAMMA_RESPONSE and COAGULATION) and signaling pathways (TNFA_SIGNALING_VIA_NKFB, IL2_STAT5_SIGNALING, KRAS_SIGNALING, ESTROGEN_REPONSE_EARLY and ESTROGEN_RESPONSE_LATE).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGenotypic patterns in large vs small tumors\u003c/h2\u003e \u003cp\u003eTo investigate if the landscape of cancer driver mutations might differ between small and large tumors, we categorized all mutations found within known cancer genes in tumors from Subset 1 into whether they were likely driver mutations or likely passenger mutations. We explored how often individual cancer driver mutations occurred together with other driver mutations within the same tumor. To investigate this, we defined a driver-weight score. The driver weight score was determined for each driver mutation, within each tumor, as simply 1/n\u003csub\u003edriver\u003c/sub\u003e. We then compared the differences in mean driver weight across genes and cancer types. We found that there were more genes with a significantly higher driver weight in small tumors relative to in large tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Examples of these are PIK3CA in both BRCA and HNSC, LRP1B in both LUSC and BRCA and TP53 in HNSC and SKCM. However, TP53 also has a higher driver weight in Large MESO tumors. To investigate whether small tumors had a higher driver weight in general, we compared the driver weight of small vs large tumors for each cancer type, where the tumor's driver weight was the same as for each of its driver mutations 1/n\u003csub\u003edriver\u003c/sub\u003e. We investigated the mean difference in driver mutations between small and large tumors and found that in three cancer types (BRCA, HNSC and KIRC) large tumors had significantly higher number of driver mutations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). When we looked at the frequency of specific driver mutations between large and small tumors, we only found two significantly enrichment genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), HRAS in small BLCA tumors and CDH1 in large BRCA tumors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study suggests a link between obesity and reduced tumor size as we found a significantly higher BMI in the patients with smaller, less invasive tumors, as represented by lower T stage. We also found an association between obesity and increased immune invasion and lower expression of proliferative pathways, suggesting that tumors in obese individuals may harbor less aggressive biology. Our results thus support previous work indicating that while the obesity induced chronic inflammatory state may support tumorigenesis, it may also limit tumor growth through immune effector mechanisms\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eFurthermore, we found an increased expression of metabolic pathways, including the fatty acid and bile acid pathway in small tumors and in obese patients. We found an increased expression of the glycolysis pathway in the large tumors. This may indicate that small tumors grow on fatty acid, whereas larger tumors preferentially utilize glucose, via glycolysis and then lactic acid fermentation, i.e. the Warburg effect\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. It is possible that tumors develop to preferentially metabolize fatty acids due to a more plentiful supply of free fatty acids in the plasma of obese patients\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003ePrevious studies have also found a high level of variation between tumors and the tumor microenvironment between men and women\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. However, we do not find this difference between men and women, when we stratify based on BMI. Here, we found no difference in BMI between male and female cancer patients in the analyzed cohort. We also found the same distribution of BMI of patients with small or large tumors in male and female patients. Furthermore, when we investigated the differentially expressed pathways between male and female patients in small or large tumors, few differences were found. This indicates that the differential expression pattern that we found in small tumors in patients with obesity, was independent of sex.\u003c/p\u003e\n\u003cp\u003eIn our study, we found that the main genetic difference between small and large tumors was the number of driver mutations, as we found fewer driver mutations per tumor in small tumors. And when we investigate if specific mutations were enriched in small or large tumors, we found only two genes, HRAS in small bladder cancer tumors and CDH1 in large breast cancer tumors. This indicates that on a genomic level, there is no difference between the molecular drivers of cancer between small and large tumors. These results thus follow the pattern of previous research, where we and others have found that there is no significant difference between the cancer driver landscape between primary and metastatic tumors\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTaken together, we here demonstrate that obesity may affect tumor biology, our findings are thus important in the context of personalized medicine. We show an effect of the host physiology on both tumor microenvironment and molecular characteristics, thus providing a more nuanced understanding of how obesity might affect cancer development. Our work thus highlights the limits of a tumor-centric approach to tumor characterization, where patient prognosis and treatment is primarily determined from single tumor biopsies. Rather, these results indicate that a holistic approach is needed, where overall patient characteristics are considered in order to properly determine optimal patient care.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJA and NJB was in charge of the study conception and design. Data analysis was done by JA, SC and IMHE. JA and SC prepared all figures. The first draft of the manuscript was written by JA and NJB, and all authors contributed to reviewing and editing the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to thank GenomeDK and Aarhus University for providing computational resources and support that contributed to these research results.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRenehan, A. G., Tyson, M., Egger, M., Heller, R. F. \u0026amp; Zwahlen, M. 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S. \u003cem\u003eet al.\u003c/em\u003e Treatment Represents a Key Driver of Metastatic Cancer Evolution. \u003cem\u003eCancer Res.\u003c/em\u003e \u003cstrong\u003e82\u003c/strong\u003e, 2918\u0026ndash;2927 (2022).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-cancer-research-and-clinical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jocr","sideBox":"Learn more about [Journal of Cancer Research and Clinical Oncology](https://www.springer.com/journal/432)","snPcode":"432","submissionUrl":"https://submission.nature.com/new-submission/432/3","title":"Journal of Cancer Research and Clinical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4459331/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4459331/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eCancer continues to be a major global health challenge, affecting millions of individuals and placing substantial burdens on healthcare systems worldwide. Recent research suggests a complex relationship between obesity and cancer, with obesity increasing the risk of various cancers while potentially improving outcomes for diagnosed patients, a phenomenon termed the \"obesity paradox\". In this study, we used a cohort of 1,781 patients to investigate the impact of obesity on tumor characteristics, including gene expression, pathway dysfunction, genetic alterations and immune infiltration.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePatient samples spanned 10 different cancer types, and were obtained from the Cancer Genome Atlas, with annotations for body mass index (BMI), age, sex, tumor size and tumor gene expression data.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWhen we compared the proportion of large (T3-T4) to small tumors (T1-T2) between obese and non-obese patients, we found that obese patients tended to present with smaller, less invasive tumors and exhibited distinct gene expression profiles, particularly in metabolic and proliferative pathways. Moreover, smaller tumors in obese patients show higher immune cell infiltration and increased T cell diversity, suggesting enhanced immune activity.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eTaken together, these findings highlight the influence of obesity on tumor biology, with implications for personalized treatment strategies that consider patient physiology alongside tumor characteristics.\u003c/p\u003e","manuscriptTitle":"Exploring the impact of body mass index on tumor biology and cancer development","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-10 18:05:27","doi":"10.21203/rs.3.rs-4459331/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-08T18:53:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-07T13:09:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69807776691876237696562289697938502555","date":"2024-05-24T19:12:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-23T07:46:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-22T15:00:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-22T15:00:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Cancer Research and Clinical Oncology","date":"2024-05-22T08:24:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-cancer-research-and-clinical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jocr","sideBox":"Learn more about [Journal of Cancer Research and Clinical Oncology](https://www.springer.com/journal/432)","snPcode":"432","submissionUrl":"https://submission.nature.com/new-submission/432/3","title":"Journal of Cancer Research and Clinical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a2a23074-47cb-4ae6-bc0e-a2dacf5b6e17","owner":[],"postedDate":"June 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-01T17:05:31+00:00","versionOfRecord":{"articleIdentity":"rs-4459331","link":"https://doi.org/10.1007/s00432-024-05890-4","journal":{"identity":"journal-of-cancer-research-and-clinical-oncology","isVorOnly":false,"title":"Journal of Cancer Research and Clinical Oncology"},"publishedOn":"2024-07-27 16:15:50","publishedOnDateReadable":"July 27th, 2024"},"versionCreatedAt":"2024-06-10 18:05:27","video":"","vorDoi":"10.1007/s00432-024-05890-4","vorDoiUrl":"https://doi.org/10.1007/s00432-024-05890-4","workflowStages":[]},"version":"v1","identity":"rs-4459331","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4459331","identity":"rs-4459331","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}